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Dynamic Copula Models and High Frequency Data

  • Irving Arturo De Lira Salvatierra
  • Andrew J. Patton

This paper proposes a new class of dynamic copula models for daily asset returns that exploits information from high frequency (intra-daily) data. We augment the generalized autoregressive score (GAS) model of Creal, et al. (2012) with high frequency measures such as realized correlation to obtain a "GRAS" model. We find that the inclusion of realized measures significantly improves the in-sample fit of dynamic copula models across a range of U.S. equity returns. Moreover, we find that out-of-sample density forecasts from our GRAS models are superior to those from simpler models. Finally, we consider a simple portfolio choice problem to illustrate the economic gains from exploiting high frequency data for modeling dynamic dependence.

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Paper provided by Duke University, Department of Economics in its series Working Papers with number 13-28.

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Length: 37
Date of creation: 2013
Date of revision:
Handle: RePEc:duk:dukeec:13-28
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